{
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  "metadata": {
    "colab": {
      "name": "Avg_std_density_layer_and_velocity_calculations",
      "provenance": [],
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    "kernelspec": {
      "name": "python3",
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          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import csv\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "!pip install basemap\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.basemap import Basemap\n",
        "import netCDF4 as nc\n",
        "!pip install xarray\n",
        "!pip install rioxarray\n",
        "import xarray as xr\n",
        "import rioxarray\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Average Calculations"
      ],
      "metadata": {
        "id": "iN1r3SrmFkeC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import meshgrid"
      ],
      "metadata": {
        "id": "Wdao4Sbh8KgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enter file path & name. Choose which variable we want to investigate."
      ],
      "metadata": {
        "id": "pavtf7q0i9FB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.rcsetup import validate_bool_maybe_none\n",
        "Thermocline=False\n",
        "MXL=True\n",
        "U=False\n",
        "V=False\n",
        "standard_cbar=False\n",
        "\n",
        "open=('/content/drive/MyDrive/REU_2022_copy/ORA-1983-2018_mixedlayer.nc')\n",
        "\n"
      ],
      "metadata": {
        "id": "YeQw-sGJimh0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "open dataset, rename variable somxl010 to mld_avg. Open dataset as xarray.\n",
        "\n",
        "Then, take the mean mld_avg aross time. So average the 35 years (time) worth of mixed layer depths for each location. \n",
        "\n"
      ],
      "metadata": {
        "id": "EzNtIXaYFsYA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stat='avg'\n",
        "year='2022'\n",
        "\n",
        "df2=xr.open_dataset(open)\n",
        "\n",
        "\n",
        "\n",
        "# thermocline\n",
        "if Thermocline:\n",
        "  depth='Thermocline'\n",
        "  plt_title='Thermocline Depth'\n",
        "  therm=df2.so20chgt\n",
        "  avg=therm.mean('time')\n",
        "  experiment='ORA_' + depth + '_' + stat\n",
        "\n",
        "# mixed layer\n",
        "elif MXL:\n",
        "  depth='MXL'\n",
        "  plt_title='Mixed Layer Depth'\n",
        "  mld=df2.somxl010\n",
        "  avg=mld.mean('time') #compute the mean over time\n",
        "  experiment='ORA_' + depth + '_' + stat\n",
        "\n",
        "# U (zonal velocity)\n",
        "\n",
        "elif U:\n",
        "  #model='ORA'\n",
        "  #vdepth = 47.21189 #1045.854 #508.6399 108.0303 1945.296 47.21189 1516.364\n",
        "  model = 'GODAS'\n",
        "  vdepth =  949  #459.0 105.0 949 2174 55 1479\n",
        "  depth = str(int(vdepth))\n",
        "  plt_title='U_zonal_Velocity'\n",
        "  #df1=df2.vozocrte #added (for ORA)\n",
        "  df1=df2.uogrddsl #added (for GODAS)\n",
        "  u=df1.drop_vars('LEV') #added (for ORA)\n",
        "  avg=u.mean('time')\n",
        "  experiment=model + '-' + year + '_' + depth + '_' + 'U' + '_' + stat\n",
        "\n",
        "# V (meridional velocity)\n",
        "elif V:\n",
        "  #model= 'ORA'\n",
        "  #vdepth = 1516.364 #1045.854 #508.6399 108.0303 1945.296 47.21189 1516.364\n",
        "  model = 'GODAS'\n",
        "  vdepth =  55  #459.0 105.0 949 2174 55 1479\n",
        "  depth = str(int(vdepth))\n",
        "  plt_title='V_meridional_Velocity'\n",
        "  #v=df2.vomecrtn #added (for ORA)\n",
        "  v=df2.vogrddsl #added (for GODAS)\n",
        "  avg=v.mean('time')\n",
        "  experiment=model + '-' + year + '_' + depth +  '_' + 'V' + '_' + stat\n",
        "\n",
        "\n",
        "print(df2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qL115RLgE8Va",
        "outputId": "d20a47d2-207e-4922-b64a-8b687407959b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.Dataset>\n",
            "Dimensions:    (time: 432, latitude: 61, longitude: 161)\n",
            "Coordinates:\n",
            "  * time       (time) datetime64[ns] 1983-01-15 1983-02-15 ... 2018-12-15\n",
            "  * latitude   (latitude) float64 0.5 1.5 2.5 3.5 4.5 ... 57.5 58.5 59.5 60.5\n",
            "  * longitude  (longitude) float64 120.0 121.0 122.0 123.0 ... 278.0 279.0 280.0\n",
            "Data variables:\n",
            "    somxl010   (time, latitude, longitude) float32 nan nan nan ... 27.51 12.33\n",
            "Attributes: (12/28)\n",
            "    cdm_data_type:              Grid\n",
            "    Conventions:                COARDS, CF-1.6, ACDD-1.3\n",
            "    creator_name:               APDRC\n",
            "    creator_type:               institution\n",
            "    creator_url:                http://apdrc.soest.hawaii.edu/thredds/dodsC/l...\n",
            "    Easternmost_Easting:        280.0\n",
            "    ...                         ...\n",
            "    standard_name_vocabulary:   CF Standard Name Table v29\n",
            "    summary:                    Reanalysis Data ORAS5 1x1 grid somxl010 opa0\n",
            "    time_coverage_end:          2018-12-15T00:00:00Z\n",
            "    time_coverage_start:        1983-01-15T00:00:00Z\n",
            "    title:                      Reanalysis Data ORAS5 1x1 grid somxl010 opa0\n",
            "    Westernmost_Easting:        120.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(avg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqN7_7ebNRyN",
        "outputId": "41f9ab06-0b92-45cd-e272-09495db6bb7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray 'somxl010' (latitude: 61, longitude: 161)>\n",
            "array([[      nan,       nan,       nan, ..., 19.04396 , 19.237135,\n",
            "              nan],\n",
            "       [18.851904, 16.106478, 15.862398, ..., 21.36301 , 20.47576 ,\n",
            "        19.107141],\n",
            "       [21.118212, 22.35952 , 21.36964 , ..., 20.693447, 20.948257,\n",
            "        21.225206],\n",
            "       ...,\n",
            "       [      nan,       nan,       nan, ..., 19.796732, 16.979963,\n",
            "        15.399329],\n",
            "       [      nan,       nan,       nan, ..., 20.289516, 17.445751,\n",
            "        15.022671],\n",
            "       [      nan,       nan,       nan, ..., 20.100197, 18.655678,\n",
            "        15.786355]], dtype=float32)\n",
            "Coordinates:\n",
            "  * latitude   (latitude) float64 0.5 1.5 2.5 3.5 4.5 ... 57.5 58.5 59.5 60.5\n",
            "  * longitude  (longitude) float64 120.0 121.0 122.0 123.0 ... 278.0 279.0 280.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.max(avg))\n",
        "print(np.min(avg))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbzJc4URcL3e",
        "outputId": "145549b6-df44-4dfe-81ea-2ab6256ae6c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray 'somxl010' ()>\n",
            "array(85.08614, dtype=float32)\n",
            "<xarray.DataArray 'somxl010' ()>\n",
            "array(5.7660885, dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a mesh grid with latitude and longitude (matrix indexing). Transpose the mld_avg matrix so that it is the same shape as the latitude and longitude matrices."
      ],
      "metadata": {
        "id": "LjVsTF38NZK5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lat,lon=meshgrid(avg['latitude'],avg['longitude'])\n",
        "#grid=np.transpose(avg.values)\n",
        "#grid1=np.transpose(avg.values) #added\n",
        "#grid=np.delete(grid1,0,2) #added... the column is deleted, but the dimensions are not changing...\n",
        "#grid1=np.transpose(avg.values)\n",
        "#grid=np.delete(grid1,0,2)\n",
        "# thermocline\n",
        "if Thermocline:\n",
        "  grid=np.transpose(avg.values)\n",
        "\n",
        "# mixed layer\n",
        "elif MXL:\n",
        "  grid=np.transpose(avg.values)\n",
        "\n",
        "# U (zonal velocity)\n",
        "elif U:\n",
        "  grid0=np.transpose(avg.values)\n",
        "  grid=np.squeeze(grid0,axis=2)\n",
        "\n",
        "# V (meridional velocity)\n",
        "elif V:\n",
        "  grid0=np.transpose(avg.values)\n",
        "  grid=np.squeeze(grid0,axis=2)\n",
        "\n",
        "lat.shape,lon.shape,grid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vo3cSnddMG9h",
        "outputId": "d79ddfdd-f74c-4b8c-bb28-c93a6d9b3458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((161, 61), (161, 61), (161, 61))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lat[0],lon[0],lat[-1],lon[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_pgbJCJNodj",
        "outputId": "1208f0ef-19d6-4a9b-8dab-3ef520715870"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5  9.5 10.5 11.5 12.5 13.5\n",
            " 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5\n",
            " 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5\n",
            " 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 55.5\n",
            " 56.5 57.5 58.5 59.5 60.5] [120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120.\n",
            " 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120.\n",
            " 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120.\n",
            " 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120. 120.\n",
            " 120. 120. 120. 120. 120.] [ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5  9.5 10.5 11.5 12.5 13.5\n",
            " 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5\n",
            " 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5\n",
            " 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 55.5\n",
            " 56.5 57.5 58.5 59.5 60.5] [280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280.\n",
            " 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280.\n",
            " 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280.\n",
            " 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280. 280.\n",
            " 280. 280. 280. 280. 280.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import BoundaryNorm\n",
        "from matplotlib import pyplot as plt,patches\n",
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "m = Basemap(projection='merc',llcrnrlon=120.0,llcrnrlat=0.0,urcrnrlon=280.0,urcrnrlat=60.0,\n",
        "                resolution='c',lat_1=0.0,lat_2=60.0,lat_0=30.0,lon_0=180.0)\n",
        "\n",
        "\n",
        "m.drawcoastlines(color='black', linewidth=0.5)\n",
        "m.fillcontinents(color='#c0c0c0', lake_color='#ffffff')\n",
        "m.drawmapboundary()\n",
        "\n",
        "m.drawparallels(np.arange(0,60,10.0),labels=[1,0,0,0])\n",
        "m.drawmeridians(np.arange(120,280,20.0),labels=[0,0,0,1])\n",
        "\n",
        "x,y=m(lon,lat) #reprojects lon and lat using above basemap projection\n",
        "if standard_cbar:\n",
        "  con=plt.contourf(x,y,grid, vmin=-1.25, vmax=0.75) # remove vmin & vmax if not standard\n",
        "else:\n",
        "  con=plt.contourf(x,y,grid)\n",
        "\n",
        "\n",
        "cbar=m.colorbar(con,'bottom',size='5%',pad='10%',cmap=plt.set_cmap('Wistia')) # Wistia for mixed layer ocean_r for thermocline\n",
        "#cbar=m.colorbar(con,'bottom',size='5%',pad='2%')\n",
        "\n",
        "#ax.add_patch(patches.Rectangle((0.25,0.25),0.50,0.50,edgecolor='black',facecolor='none',linewidth=2))\n",
        "\n",
        "if standard_cbar:\n",
        "  plt.title('Average ' + plt_title + ' at ' + depth + ' meters (Standard cbar)')\n",
        "  plt.savefig('/content/drive/MyDrive/REU_2022_copy/plots_copy/Avg_and_STD_plots/' + experiment + '_standard_cbar' + '.png', format='png') # remove '_standard_cbar' if not standard\n",
        "\n",
        "else:\n",
        "  plt.title('Average ' + plt_title)\n",
        "  #plt.title('Average ' + plt_title + ' at ' + depth + ' meters') # VELOCITY\n",
        "  plt.savefig('/content/drive/MyDrive/REU_2022_copy/plots_copy/Avg_and_STD_plots/' + experiment + 'poster.png', format='png')\n",
        " \n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "KcM3OobHHLnx",
        "outputId": "9d96d010-2dce-4d7f-fab0-6a8d9495fd00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "End Averages Calculations & Plots"
      ],
      "metadata": {
        "id": "BMEGzVRxN2Ci"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Standard Deviation Calcluations"
      ],
      "metadata": {
        "id": "bAyLB7mpOjZq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stat='std'\n",
        "year='2022'\n",
        "\n",
        "# thermocline\n",
        "if Thermocline:  \n",
        "  plt_title='Thermocline' \n",
        "  depth='Thermocline' \n",
        "  experiment1= 'ORA_'+ depth +'_' + stat\n",
        "  therm=df2.so20chgt\n",
        "  std=therm.std('time')\n",
        "\n",
        "# mixed layer\n",
        "elif MXL:\n",
        "  plt_title='MXL' # mxl thermocline\n",
        "  depth='MXL'\n",
        "  experiment1= 'ORA_'+ depth +'_' + stat\n",
        "  mld=df2.somxl010\n",
        "  std=mld.std('time') # compute mean over time\n",
        "\n",
        "# U (zonal velocity)\n",
        "elif U:\n",
        "  plt_title='U_zonal_Velocity'\n",
        "  #model='ORA'\n",
        "  #vdepth = 47.21189  #1045.854 #508.6399 108.0303 1945.296 47.21189 1516.364\n",
        "  model = 'GODAS'\n",
        "  vdepth =  949  #459.0 105.0 949 2174 55 1479\n",
        "  depth = str(int(vdepth))\n",
        "  experiment1=model + '-' + year + '_' + depth + '_' + 'U' + '_'  + stat\n",
        "  #u=df2.vozocrte #added for (ORA)\n",
        "  u=df2.uogrddsl #added (for GODAS)\n",
        "  std=u.std('time')\n",
        "\n",
        "\n",
        "# V (meridional velocity)\n",
        "elif V:\n",
        "  plt_title='V_meridional_Velocity'\n",
        "  #model='ORA'\n",
        "  #vdepth = 1516.364 #1045.854 #508.6399 108.0303 1945.296 47.21189 1516.364\n",
        "  model = 'GODAS'\n",
        "  vdepth =  55  #459.0 105.0 949 2174 55 1479\n",
        "  depth = str(int(vdepth))\n",
        "  plt_title='V_meridional_'\n",
        "  #v=df2.vomecrtn #added (for ORA)\n",
        "  v=df2.vogrddsl #added (for GODAS)\n",
        "  std=v.std('time')\n",
        "  experiment1=model + '-' + year + '_' + depth + '_' + 'V' + '_' + stat\n"
      ],
      "metadata": {
        "id": "JWgKJF_UIOKt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5c49866-d45c-48b0-cca4-accd383b60f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
            "  keepdims=keepdims)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TdgiBmLCJIG",
        "outputId": "caac539b-c253-4033-ad0d-a196c4969914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray 'somxl010' (latitude: 61, longitude: 161)>\n",
            "array([[      nan,       nan,       nan, ..., 5.763731 , 5.5886283,\n",
            "              nan],\n",
            "       [4.871517 , 4.927441 , 4.4295034, ..., 7.775425 , 7.612421 ,\n",
            "        6.671871 ],\n",
            "       [6.48569  , 6.9740577, 6.245818 , ..., 8.257203 , 8.496143 ,\n",
            "        8.777061 ],\n",
            "       ...,\n",
            "       [      nan,       nan,       nan, ..., 7.846962 , 5.8915644,\n",
            "        5.044096 ],\n",
            "       [      nan,       nan,       nan, ..., 7.532911 , 5.723454 ,\n",
            "        3.525115 ],\n",
            "       [      nan,       nan,       nan, ..., 7.0370607, 6.4319205,\n",
            "        4.586317 ]], dtype=float32)\n",
            "Coordinates:\n",
            "  * latitude   (latitude) float64 0.5 1.5 2.5 3.5 4.5 ... 57.5 58.5 59.5 60.5\n",
            "  * longitude  (longitude) float64 120.0 121.0 122.0 123.0 ... 278.0 279.0 280.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.max(std))\n",
        "print(np.min(std))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jATR-R1zb98F",
        "outputId": "ab5453b4-03c5-4036-8d72-f7e7d793ee38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray 'somxl010' ()>\n",
            "array(86.70214, dtype=float32)\n",
            "<xarray.DataArray 'somxl010' ()>\n",
            "array(5.271633e-07, dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "lat,lon=meshgrid(std['latitude'],std['longitude'])\n",
        "grid_std=np.transpose(std.values)\n",
        "\n",
        "lat.shape,lon.shape,grid_std.shape\n",
        "\n",
        "lat,lon=meshgrid(std['latitude'],std['longitude'])\n",
        "#grid=np.transpose(avg.values)\n",
        "#grid1=np.transpose(avg.values) #added\n",
        "#grid=np.delete(grid1,0,2) #added... the column is deleted, but the dimensions are not changing...\n",
        "#grid1=np.transpose(avg.values)\n",
        "#grid=np.delete(grid1,0,2)\n",
        "'''\n",
        "# thermocline\n",
        "if Thermocline:\n",
        "  grid_std=np.transpose(std.values)\n",
        "\n",
        "# mixed layer\n",
        "elif MXL:\n",
        "  grid_std=np.transpose(std.values)\n",
        "\n",
        "# U (zonal velocity)\n",
        "elif U:\n",
        "  grid0=np.transpose(std.values)\n",
        "  grid_std=np.squeeze(grid0,axis=2)\n",
        "\n",
        "# V (meridional velocity)\n",
        "elif V:\n",
        "  grid0=np.transpose(std.values)\n",
        "  grid_std=np.squeeze(grid0,axis=2)\n",
        "\n",
        "lat.shape,lon.shape,grid_std.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGr5mvWiO3_1",
        "outputId": "58371a22-ac5d-437c-f112-3a8be777cae2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((161, 61), (161, 61), (161, 61))"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "m2 = Basemap(projection='merc',llcrnrlon=120.0,llcrnrlat=0.0,urcrnrlon=280.0,urcrnrlat=60.0,\n",
        "                resolution='c',lat_1=0.0,lat_2=60.0,lat_0=30.0,lon_0=180.0)\n",
        "\n",
        "\n",
        "\n",
        "m2.drawcoastlines(color='black', linewidth=0.5)\n",
        "m2.fillcontinents(color='#c0c0c0', lake_color='#ffffff')\n",
        "m2.drawmapboundary()\n",
        "\n",
        "m2.drawparallels(np.arange(0,60,10.0),labels=[1,0,0,0])\n",
        "m2.drawmeridians(np.arange(120,280,20.0),labels=[0,0,0,1])\n",
        "\n",
        "x,y=m2(lon,lat) #reprojects lon and lat using above basemap projection\n",
        "if standard_cbar: \n",
        "  con=plt.contourf(x,y,grid_std, vmin=0, vmax=0.40) # remove vmin & vmax if not looking for standard\n",
        "else:\n",
        "  con=plt.contourf(x,y,grid_std)\n",
        "\n",
        "cbar=m2.colorbar(con,'bottom',size='5%',pad='2%')\n",
        "\n",
        "if standard_cbar:\n",
        "  plt.title('Standard Deviation of ' + plt_title + '_' + depth + 'Standard Cbar')\n",
        "  plt.savefig('/content/drive/MyDrive/REU_2022_copy/plots_copy/Avg_and_STD_plots/' + experiment1 + '_standard_cbar' + '.png', format='png') # remove '_standard_cbar' if not standard\n",
        "else:\n",
        "  plt.title('Standard Deviation of ' + plt_title + '_' + depth)\n",
        "  plt.savefig('/content/drive/MyDrive/REU_2022_copy/plots_copy/Avg_and_STD_plots/' + experiment1 + '.png', format='png')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "4gdW8ESUPBE0",
        "outputId": "82842c48-7649-4ba5-c592-116d52481362"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qO6bC9t-Pe8E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}